Career Architect
Visualize your path in IT. From basics to Senior level.
Math & Algorithms
0Linear Algebra, Calculus, Python OOP, Big O. Фундамент для понимания ML-моделей.
SQL Advanced
Window Functions, CTEs, Query Optimization. Работа с реляционными БД.
Pandas & NumPy
Data Manipulation, Vectorization, Performance. Основа для работы с данными в Python.
Vector DBs
Qdrant, Pinecone, Weaviate. Специализированные БД для embeddings и RAG.
Regression & Classification
Linear/Logistic Regression, Decision Trees, Random Forest. Базовые ML-алгоритмы.
Scikit-learn
Pipelines, Cross-Validation, Feature Engineering. Production-ready ML библиотека.
Metrics & Validation
Precision/Recall, F1, ROC-AUC, Cross-Validation. Оценка качества моделей.
Neural Networks
Feedforward, Backpropagation, Activation Functions. Основы глубокого обучения.
CNNs
Convolutional Layers, Pooling, Image Classification. Компьютерное зрение.
PyTorch
Tensors, Autograd, Training Loops. Фреймворк для глубокого обучения.
Transformers
Attention Mechanism, BERT, GPT Architecture. Основа современных LLM.
LangChain
Chains, Agents, Memory. Фреймворк для LLM-приложений.
RAG Systems
Retrieval-Augmented Generation, Embeddings, Chunking. RAG архитектура.
Fine-Tuning (LoRA)
Parameter-Efficient Fine-Tuning, LoRA, QLoRA. Адаптация LLM под задачи.
AI Agents
Autonomous Agents, Tool Use, Multi-Agent Systems. Автономные AI-системы.
Docker
Containers, Images, Docker Compose. Контейнеризация ML-приложений.
Kubernetes
K8s, Pods, Services, Deployments. Оркестрация ML-инфраструктуры.
FastAPI Serving
API Endpoints, Model Serving, Async Requests. Production API для ML-моделей.
MLflow
Experiment Tracking, Model Registry, Deployment. Управление ML-жизненным циклом.
AI & ML Engineer Roadmap 2025
This is your personalized roadmap. Click on nodes to see recommended materials.